Hi Livia,

[1] only the intercept is used as random effect in the model

[1 2 ] both intercept and time (slopes) are used as random effect in the model. Here many more parameters get estimated so you need to have sufficient data. Often the more simple approach is better (but that can be tested by comparing the models, described on the wiki).

FDR correction can be done independent of the vertex wise or region wise approach.

The region-wise approach may be better (it definitely should be faster, but also benefits from some more statistical power).

this is a LME model, which is different from standard  GLM models.  The interaction of group and time is slope (e.g. atroph rate) difference across groups ( C = [ 0 0 0 1]). I explained it in detail in my last email.

Best, Martin

Dear Martin,
Thank you very much for your quick reply. 

I use voxel-wise mixed model analysis, and I see different design:

lhstats = lme_mass_fit_vw(X,[1],Y,ni,lhcortex);----Summary: Algorithm did not converge at 0 percent of the total number of locations.
lhstats = lme_mass_fit_vw(X,[1 2],Y,ni,lhcortex);----Summary: Algorithm did not converge at 9.231.. percent of...

What the meaning and difference between [1] [1 2] or other?

In the end of the page https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels 

It use Region-wise linear mixed-effects estimation:lme_mass_fit_Rgw , then do FDR. 

Sorry, I can not make sure how to do FDR in my result.Please tell me some details.

And should I try this Region-wise model in my analysis? 

My purpose is to compare cortical thickness changes in patient group after treatment and  placebo group in 2 time points.
Then You say in the second mail:(if longitudinal slopes = atrophy rates, differ across groups),I am so sorry that I can't understand. 
If I get the interaction result, how to explain?
I wonder I must get the main effect about the time or group to measure what cause the interaction. 
 
Could you please give me some direction? Looking forward to reply, and thanks very much.
Kind regards,
Livia

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